Instructions to use internlm/internlm2_5-7b-chat-1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm2_5-7b-chat-1m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="internlm/internlm2_5-7b-chat-1m", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("internlm/internlm2_5-7b-chat-1m", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/internlm2_5-7b-chat-1m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2_5-7b-chat-1m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2_5-7b-chat-1m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/internlm/internlm2_5-7b-chat-1m

SGLang

How to use internlm/internlm2_5-7b-chat-1m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "internlm/internlm2_5-7b-chat-1m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2_5-7b-chat-1m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "internlm/internlm2_5-7b-chat-1m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2_5-7b-chat-1m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use internlm/internlm2_5-7b-chat-1m with Docker Model Runner:
```
docker model run hf.co/internlm/internlm2_5-7b-chat-1m
```

1M inference error on A100 80GBx4 System

by shi3z - opened Jul 4, 2024

Discussion

shi3z

Jul 4, 2024

Thank you for the excellent results. I immediately tried to run it on my 8x A100 80GB system, but I encountered this error. Do you know of any solutions?

>>> pipe = pipeline('internlm/internlm2_5-7b-chat-1m', backend_config=backend_config)
Fetching 20 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 6944.21it/s]
[WARNING] gemm_config.in is not found; using default GEMM algo                                                                                                                               
[WARNING] gemm_config.in is not found; using default GEMM algo
[WARNING] gemm_config.in is not found; using default GEMM algo
[WARNING] gemm_config.in is not found; using default GEMM algo
[WARNING] gemm_config.in is not found; using default GEMM algo
Exception in thread Thread-35:
Traceback (most recent call last):
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 980, in _bootstrap_inner
    self.run()
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 917, in run
    self._target(*self._args, **self._kwargs)
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/turbomind/turbomind.py", line 398, in _create_model_instance
    model_inst = self.tm_model.model_comm.create_model_instance(
RuntimeError: [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/allocator.h:231 

[WARNING] gemm_config.in is not found; using default GEMM algo
Exception in thread Thread-37:
Traceback (most recent call last):
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 980, in _bootstrap_inner
    self.run()
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 917, in run
    self._target(*self._args, **self._kwargs)
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/turbomind/turbomind.py", line 398, in _create_model_instance
    model_inst = self.tm_model.model_comm.create_model_instance(
RuntimeError: [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/allocator.h:231 

[WARNING] gemm_config.in is not found; using default GEMM algo
Exception in thread Thread-36:
Traceback (most recent call last):
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 980, in _bootstrap_inner
    self.run()
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 917, in run
    self._target(*self._args, **self._kwargs)
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/turbomind/turbomind.py", line 398, in _create_model_instance
    model_inst = self.tm_model.model_comm.create_model_instance(
RuntimeError: [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/allocator.h:231 

[WARNING] gemm_config.in is not found; using default GEMM algo
Exception in thread Thread-38:
Traceback (most recent call last):
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 980, in _bootstrap_inner
    self.run()
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 917, in run
    self._target(*self._args, **self._kwargs)
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/turbomind/turbomind.py", line 398, in _create_model_instance
    model_inst = self.tm_model.model_comm.create_model_instance(
RuntimeError: [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/allocator.h:231 

^CTraceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/api.py", line 89, in pipeline
    return pipeline_class(model_path,
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/serve/async_engine.py", line 217, in __init__
    self.gens_set.add(self.engine.create_instance())
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/turbomind/turbomind.py", line 358, in create_instance
    return TurboMindInstance(self, cuda_stream_id)
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/site-packages/lmdeploy/turbomind/turbomind.py", line 390, in __init__
    t.join()
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 1060, in join
    self._wait_for_tstate_lock()
  File "/home/shi3z/.pyenv/versions/anaconda3-2023.09-0/envs/vllm/lib/python3.9/threading.py", line 1080, in _wait_for_tstate_lock
    if lock.acquire(block, timeout):

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Intern Large Models org Jul 4, 2024

Can you share the backend_config?

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